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Article ; Online: Differential privacy in collaborative filtering recommender systems: a review.

Müllner, Peter / Lex, Elisabeth / Schedl, Markus / Kowald, Dominik

Frontiers in big data

2023 Volume 6, Page(s) 1249997

Abstract: State-of-the-art recommender systems produce high-quality recommendations to support users in finding relevant content. However, through the utilization of users' data for generating recommendations, recommender systems threaten users' privacy. To ... ...

Abstract	State-of-the-art recommender systems produce high-quality recommendations to support users in finding relevant content. However, through the utilization of users' data for generating recommendations, recommender systems threaten users' privacy. To alleviate this threat, often, differential privacy is used to protect users' data via adding random noise. This, however, leads to a substantial drop in recommendation quality. Therefore, several approaches aim to improve this trade-off between accuracy and user privacy. In this work, we first overview threats to user privacy in recommender systems, followed by a brief introduction to the differential privacy framework that can protect users' privacy. Subsequently, we review recommendation approaches that apply differential privacy, and we highlight research that improves the trade-off between recommendation quality and user privacy. Finally, we discuss open issues, e.g., considering the relation between privacy and fairness, and the users' different needs for privacy. With this review, we hope to provide other researchers an overview of the ways in which differential privacy has been applied to state-of-the-art collaborative filtering recommender systems.
Language	English
Publishing date	2023-10-12
Publishing country	Switzerland
Document type	Journal Article ; Review
ISSN	2624-909X
ISSN (online)	2624-909X
DOI	10.3389/fdata.2023.1249997
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Investigating country-specific music preferences and music recommendation algorithms with the LFM-1b dataset.

Schedl, Markus

International journal of multimedia information retrieval

2017 Volume 6, Issue 1, Page(s) 71–84

Abstract: Recently, the LFM-1b dataset has been proposed to foster research and evaluation in music retrieval and music recommender systems, Schedl (Proceedings of the ACM International Conference on Multimedia Retrieval (ICMR). New York, 2016). It contains more ... ...

Abstract	Recently, the LFM-1b dataset has been proposed to foster research and evaluation in music retrieval and music recommender systems, Schedl (Proceedings of the ACM International Conference on Multimedia Retrieval (ICMR). New York, 2016). It contains more than one billion music listening events created by more than 120,000 users of Last.fm. Each listening event is characterized by artist, album, and track name, and further includes a timestamp. Basic demographic information and a selection of more elaborate listener-specific descriptors are included as well, for anonymized users. In this article, we reveal information about LFM-1b's acquisition and content and we compare it to existing datasets. We furthermore provide an extensive statistical analysis of the dataset, including basic properties of the item sets, demographic coverage, distribution of listening events (e.g., over artists and users), and aspects related to music preference and consumption behavior (e.g., temporal features and mainstreaminess of listeners). Exploiting country information of users and genre tags of artists, we also create taste profiles for populations and determine similar and dissimilar countries in terms of their populations' music preferences. Finally, we illustrate the dataset's usage in a simple artist recommendation task, whose results are intended to serve as baseline against which more elaborate techniques can be assessed.
Language	English
Publishing date	2017-02-06
Publishing country	England
Document type	Journal Article
ZDB-ID	2649485-1
ISSN	2192-662X ; 2192-6611
ISSN (online)	2192-662X
ISSN	2192-6611
DOI	10.1007/s13735-017-0118-y
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Book ; Online: The Impact of Differential Privacy on Recommendation Accuracy and Popularity Bias

Müllner, Peter / Lex, Elisabeth / Schedl, Markus / Kowald, Dominik

2024

Abstract: Collaborative filtering-based recommender systems leverage vast amounts of behavioral user data, which poses severe privacy risks. Thus, often, random noise is added to the data to ensure Differential Privacy (DP). However, to date, it is not well ... ...

Abstract	Collaborative filtering-based recommender systems leverage vast amounts of behavioral user data, which poses severe privacy risks. Thus, often, random noise is added to the data to ensure Differential Privacy (DP). However, to date, it is not well understood, in which ways this impacts personalized recommendations. In this work, we study how DP impacts recommendation accuracy and popularity bias, when applied to the training data of state-of-the-art recommendation models. Our findings are three-fold: First, we find that nearly all users' recommendations change when DP is applied. Second, recommendation accuracy drops substantially while recommended item popularity experiences a sharp increase, suggesting that popularity bias worsens. Third, we find that DP exacerbates popularity bias more severely for users who prefer unpopular items than for users that prefer popular items. Comment: Accepted at the IR4Good track at ECIR'24, 17 pages
Keywords	Computer Science - Information Retrieval
Subject code	303
Publishing date	2024-01-08
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Effective Controllable Bias Mitigation for Classification and Retrieval using Gate Adapters

Masoudian, Shahed / Volaucnik, Cornelia / Schedl, Markus

2024

Abstract: Bias mitigation of Language Models has been the topic of many studies with a recent focus on learning separate modules like adapters for on-demand debiasing. Besides optimizing for a modularized debiased model, it is often critical in practice to control ...

Abstract	Bias mitigation of Language Models has been the topic of many studies with a recent focus on learning separate modules like adapters for on-demand debiasing. Besides optimizing for a modularized debiased model, it is often critical in practice to control the degree of bias reduction at inference time, e.g., in order to tune for a desired performance-fairness trade-off in search results or to control the strength of debiasing in classification tasks. In this paper, we introduce Controllable Gate Adapter (ConGater), a novel modular gating mechanism with adjustable sensitivity parameters, which allows for a gradual transition from the biased state of the model to the fully debiased version at inference time. We demonstrate ConGater performance by (1) conducting adversarial debiasing experiments with three different models on three classification tasks with four protected attributes, and (2) reducing the bias of search results through fairness list-wise regularization to enable adjusting a trade-off between performance and fairness metrics. Our experiments on the classification tasks show that compared to baselines of the same caliber, ConGater can maintain higher task performance while containing less information regarding the attributes. Our results on the retrieval task show that the fully debiased ConGater can achieve the same fairness performance while maintaining more than twice as high task performance than recent strong baselines. Overall, besides strong performance ConGater enables the continuous transitioning between biased and debiased states of models, enhancing personalization of use and interpretability through controllability.
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Computers and Society
Subject code	006
Publishing date	2024-01-29
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Exploring emotions in Bach chorales: a multi-modal perceptual and data-driven study.

Parada-Cabaleiro, Emilia / Batliner, Anton / Zentner, Marcel / Schedl, Markus

Royal Society open science

2023 Volume 10, Issue 12, Page(s) 230574

Abstract: The relationship between music and emotion has been addressed within several disciplines, from more historico-philosophical and anthropological ones, such as musicology and ethnomusicology, to others that are traditionally more empirical and ... ...

Abstract	The relationship between music and emotion has been addressed within several disciplines, from more historico-philosophical and anthropological ones, such as musicology and ethnomusicology, to others that are traditionally more empirical and technological, such as psychology and computer science. Yet, understanding the link between music and emotion is limited by the scarce interconnections between these disciplines. Trying to narrow this gap, this data-driven exploratory study aims at assessing the relationship between linguistic, symbolic and acoustic features-extracted from lyrics, music notation and audio recordings-and perception of emotion. Employing a listening experiment, statistical analysis and unsupervised machine learning, we investigate how a data-driven multi-modal approach can be used to explore the emotions conveyed by eight Bach chorales. Through a feature selection strategy based on a set of more than 300 Bach chorales and a transdisciplinary methodology integrating approaches from psychology, musicology and computer science, we aim to initiate an efficient dialogue between disciplines, able to promote a more integrative and holistic understanding of emotions in music.
Language	English
Publishing date	2023-12-20
Publishing country	England
Document type	Journal Article
ZDB-ID	2787755-3
ISSN	2054-5703
ISSN	2054-5703
DOI	10.1098/rsos.230574
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Article ; Online: Fairness of recommender systems in the recruitment domain: an analysis from technical and legal perspectives.

Kumar, Deepak / Grosz, Tessa / Rekabsaz, Navid / Greif, Elisabeth / Schedl, Markus

Frontiers in big data

2023 Volume 6, Page(s) 1245198

Abstract: Recommender systems (RSs) have become an integral part of the hiring process, be it via job advertisement ranking systems (job recommenders) for the potential employee or candidate ranking systems (candidate recommenders) for the employer. As seen in ... ...

Abstract	Recommender systems (RSs) have become an integral part of the hiring process, be it via job advertisement ranking systems (job recommenders) for the potential employee or candidate ranking systems (candidate recommenders) for the employer. As seen in other domains, RSs are prone to harmful biases, unfair algorithmic behavior, and even discrimination in a legal sense. Some cases, such as salary equity in regards to gender (gender pay gap), stereotypical job perceptions along gendered lines, or biases toward other subgroups sharing specific characteristics in candidate recommenders, can have profound ethical and legal implications. In this survey, we discuss the current state of fairness research considering the fairness definitions (e.g., demographic parity and equal opportunity) used in recruitment-related RSs (RRSs). We investigate from a technical perspective the approaches to improve fairness, like synthetic data generation, adversarial training, protected subgroup distributional constraints, and
Language	English
Publishing date	2023-10-06
Publishing country	Switzerland
Document type	Journal Article ; Review
ISSN	2624-909X
ISSN (online)	2624-909X
DOI	10.3389/fdata.2023.1245198
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: An Exploratory Study on the Acoustic Musical Properties to Decrease Self-Perceived Anxiety.

Parada-Cabaleiro, Emilia / Batliner, Anton / Schedl, Markus

International journal of environmental research and public health

2022 Volume 19, Issue 2

Abstract: Musical listening is broadly used as an inexpensive and safe method to reduce self-perceived anxiety. This strategy is based on ... ...

Abstract	Musical listening is broadly used as an inexpensive and safe method to reduce self-perceived anxiety. This strategy is based on the
MeSH term(s)	Acoustic Stimulation ; Acoustics ; Anxiety/prevention & control ; Auditory Perception ; Emotions ; Humans ; Music/psychology
Language	English
Publishing date	2022-01-16
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2175195-X
ISSN	1660-4601 ; 1661-7827
ISSN (online)	1660-4601
ISSN	1661-7827
DOI	10.3390/ijerph19020994
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Book ; Online: A Study on Accuracy, Miscalibration, and Popularity Bias in Recommendations

Kowald, Dominik / Mayr, Gregor / Schedl, Markus / Lex, Elisabeth

2023

Abstract: Recent research has suggested different metrics to measure the inconsistency of recommendation performance, including the accuracy difference between user groups, miscalibration, and popularity lift. However, a study that relates miscalibration and ... ...

Abstract	Recent research has suggested different metrics to measure the inconsistency of recommendation performance, including the accuracy difference between user groups, miscalibration, and popularity lift. However, a study that relates miscalibration and popularity lift to recommendation accuracy across different user groups is still missing. Additionally, it is unclear if particular genres contribute to the emergence of inconsistency in recommendation performance across user groups. In this paper, we present an analysis of these three aspects of five well-known recommendation algorithms for user groups that differ in their preference for popular content. Additionally, we study how different genres affect the inconsistency of recommendation performance, and how this is aligned with the popularity of the genres. Using data from LastFm, MovieLens, and MyAnimeList, we present two key findings. First, we find that users with little interest in popular content receive the worst recommendation accuracy, and that this is aligned with miscalibration and popularity lift. Second, our experiments show that particular genres contribute to a different extent to the inconsistency of recommendation performance, especially in terms of miscalibration in the case of the MyAnimeList dataset. Comment: Accepted at BIAS@ECIR WS 2023
Keywords	Computer Science - Information Retrieval ; Computer Science - Artificial Intelligence ; Computer Science - Machine Learning
Subject code	006
Publishing date	2023-03-01
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Global and country-specific mainstreaminess measures: Definitions, analysis, and usage for improving personalized music recommendation systems.

Bauer, Christine / Schedl, Markus

PloS one

2019 Volume 14, Issue 6, Page(s) e0217389

Abstract: Relevance: Popularity-based approaches are widely adopted in music recommendation systems, both in industry and research. These approaches recommend to the target user what is currently popular among all users of the system. However, as the popularity ... ...

Abstract	Relevance: Popularity-based approaches are widely adopted in music recommendation systems, both in industry and research. These approaches recommend to the target user what is currently popular among all users of the system. However, as the popularity distribution of music items typically is a long-tail distribution, popularity-based approaches to music recommendation fall short in satisfying listeners that have specialized music preferences far away from the global music mainstream. Addressing this gap, the contribution of this article is three-fold. Definition of mainstreaminess measures: First, we provide several quantitative measures describing the proximity of a user's music preference to the music mainstream. Assuming that there is a difference between the global music mainstream and a country-specific one, we define the measures at two levels: relating a listener's music preferences to the global music preferences of all users, or relating them to music preferences of the user's country. To quantify such music preferences, we define a music item's popularity in terms of artist playcounts (APC) and artist listener counts (ALC). Moreover, we adopt a distribution-based and a rank-based approach as means to decrease bias towards the head of the long-tail distribution. This eventually results in a framework of 6 measures to quantify music mainstream. Differences between countries with respect to music mainstream: Second, we perform in-depth quantitative and qualitative studies of music mainstream in that we (i) analyze differences between countries in terms of their level of mainstreaminess, (ii) uncover both positive and negative outliers (substantially higher and lower country-specific popularity, respectively, compared to the global mainstream), analyzing these with a mixed-methods approach, and (iii) investigate differences between countries in terms of listening preferences related to popular music artists. We conduct our studies and experiments using the standardized LFM-1b dataset, from which we analyze about 800,000,000 listening events shared by about 53,000 users (from 47 countries) of the music streaming platform Last.fm. We show that there are substantial country-specific differences in listeners' music consumption behavior with respect to the most popular artists listened to. Rating prediction experiments: Third, we demonstrate the applicability of our study results to improve music recommendation systems. To this end, we conduct rating prediction experiments in which we tailor recommendations to a user's level of preference for the music mainstream using the proposed 6 mainstreaminess measures: defined by a distribution-based or rank-based approach, defined on a global level or on a country level (for the user's country), and for APC or ALC. Our approach roughly equals a hybrid recommendation approach in which a demographic filtering strategy is implemented before collaborative filtering is performed. Results suggest that, in terms of rating prediction accuracy, each of the presented mainstreaminess definitions has its merits.
MeSH term(s)	Databases, Factual ; Humans ; Internet ; Music ; Social Media
Language	English
Publishing date	2019-06-07
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1932-6203
ISSN (online)	1932-6203
DOI	10.1371/journal.pone.0217389
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Song lyrics have become simpler and more repetitive over the last five decades.

Parada-Cabaleiro, Emilia / Mayerl, Maximilian / Brandl, Stefan / Skowron, Marcin / Schedl, Markus / Lex, Elisabeth / Zangerle, Eva

Scientific reports

2024 Volume 14, Issue 1, Page(s) 5531

Abstract: Music is ubiquitous in our everyday lives, and lyrics play an integral role when we listen to music. The complex relationships between lyrical content, its temporal evolution over the last decades, and genre-specific variations, however, are yet to be ... ...

Abstract	Music is ubiquitous in our everyday lives, and lyrics play an integral role when we listen to music. The complex relationships between lyrical content, its temporal evolution over the last decades, and genre-specific variations, however, are yet to be fully understood. In this work, we investigate the dynamics of English lyrics of Western, popular music over five decades and five genres, using a wide set of lyrics descriptors, including lyrical complexity, structure, emotion, and popularity. We find that pop music lyrics have become simpler and easier to comprehend over time: not only does the lexical complexity of lyrics decrease (for instance, captured by vocabulary richness or readability of lyrics), but we also observe that the structural complexity (for instance, the repetitiveness of lyrics) has decreased. In addition, we confirm previous analyses showing that the emotion described by lyrics has become more negative and that lyrics have become more personal over the last five decades. Finally, a comparison of lyrics view counts and listening counts shows that when it comes to the listeners' interest in lyrics, for instance, rock fans mostly enjoy lyrics from older songs; country fans are more interested in new songs' lyrics.
MeSH term(s)	Music/psychology ; Emotions ; Vocabulary
Language	English
Publishing date	2024-03-28
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-024-55742-x
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